LFM2.5-2.6B Matches DeepSeek-V4-Flash on Tool Calling; LEAP Fine-Tuning

Melvin Vivas · X post · 2026-08-08 · Open on X

Topics: AI Agents, Tool Use & MCP, Fine-tuning & Model Customization, Evaluation (Evals) & Testing · Level: intermediate

Summary

The creator is trying Liquid AI's LEAP framework for fine-tuning and says it looks like it has everything you need. He quotes a benchmark where Liquid AI's LFM2.5-2.6B was run against DeepSeek-V4-Flash on one machine with 4x RTX 5090. Each job only counted as complete if the model made every required tool call, and the small model reportedly matched DeepSeek-V4 on tool calling while running 3.7x faster.

Key points

• LFM2.5-2.6B is a small Liquid AI model that reportedly reaches DeepSeek-V4 level on tool calling.
• It reportedly ran 3.7x faster than DeepSeek-V4-Flash on the same hardware (4x RTX 5090).
• Test method: both models got the same three jobs, and a job only completed if the model made every tool call.
• Example job topics included a weather task.
• LEAP is Liquid AI's fine-tuning framework, which the creator says looks like it has everything you need.

Resources mentioned

• Liquid AI (@liquidai) on X · website · x.com · free
  An AI company that builds efficient foundation models. The quoted post shows its PII handling working on Japanese text.
  Also in: Zero-Shot Prompt Routing with Liquid AI's LFM 2.5-Encoder-350M (Melvin Vivas on X · notes), Liquid AI LFM 2.5 Encoder: a CPU-friendly encoder model (Melvin Vivas on X · notes), Fine-tuning Liquid AI LFM2/LFM2.5 MoE models with the new Halo framework (Melvin Vivas on X · notes), Liquid AI's LFM2-Longevity models for aging-data analysis (Melvin Vivas on X · notes) and 19 more
• LEAP (Liquid AI) · tool · leap.liquid.ai · check price
  Liquid AI's framework for fine-tuning and customizing its small models.
  Also in: Smoke-Testing LFM Fine-tuning with Codex and Liquid AI's LEAP (Melvin Vivas on X · notes), Small Local Models + Fine-Tuning Instead of More Compute (Liquid AI, LEAP) (Melvin Vivas on X · notes)
• LFM2.5-2.6B · tool · huggingface.co · free
  Liquid AI's small language model, which can be paired with the LFM2.5-VL-3B vision model.
  Also in: Coworker: Open-Source Work Agent That Runs on Small Local Models (Melvin Vivas on X · notes), Coworker with Liquid AI LFM2.5-2.6B via LM Studio on a Mac (Melvin Vivas on X · notes), Zero-Cost Coworker Setup: OpenRouter Free Models + Local LFM2.5 (Melvin Vivas on X · notes), Coworker: A Subscription-Free AI Agent on Local LFM2.5-2.6B (Melvin Vivas on X · notes) and 9 more
• DeepSeek V4 Flash · tool · huggingface.co · free
  DeepSeek model used as the comparison baseline in the tool-calling benchmark.
  Also in: Low-Cost Agent Run: DeepSeek V4 Flash via OpenRouter in ohmypi (Melvin Vivas on X · notes), Running Codex with DeepSeek V4 Flash through OpenRouter (Melvin Vivas on X · notes), DeepSeek V4 Flash at 90% Off on Nous Portal (Melvin Vivas on X · notes), Qwen3.8-Max on the Frontend Code Arena cost-performance frontier (Melvin Vivas on X · notes) and 3 more

Try this

• Check out Liquid AI's LEAP framework for fine-tuning.
• Try LFM2.5-2.6B for tool-calling tasks.
• Build a small tool-calling benchmark: give a small model and a large model the same multi-tool jobs, count a job as passed only if every required tool call is made, and compare pass rate and speed.